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KHAN, M., ŽÁK, L., ONDRŮŠEK, Č.
Original Title
FUZZY-NEURAL NETWORK BASED SHORT-TERM SEASONAL AND AVERAGE LOAD FORECASTING
English Title
Type
Paper in proceedings (conference paper)
Original Abstract
A hybrid approach utilizing a fuzzy system and artificial neural network for short-term average and seasonal load prediction is proposed for the Czech Electric Power Utility (ČEZ), Czech Republic in this paper. The FNN is trained on real data and evaluated for forecasting seasonal and average load profiles based on forecast weather data. The fuzzy membership values of the load and weather variables are the inputs to the hybrid fuzzy-neural network (FNN) and the output is the predicted load. The performance of this network has been compared with ANN technique in order to demonstrate the superiority of this approach.
English abstract
Key words in English
Hybrid fuzzy-neural network (FNN), Short-term average and seasonal load forecasting, artificial neural networks (ANN).
Authors
Released
25.09.2001
Publisher
n
Location
Zlín
ISBN
80-7318-030-8
Book
4th International Conference on Prediction and Nonlinear Dynamics, Nostradamus Prediction Conference
Pages from
13
Pages count
1
BibTex
@inproceedings{BUT3902, author="Muhammad R {Khan} and Libor {Žák} and Čestmír {Ondrůšek}", title="FUZZY-NEURAL NETWORK BASED SHORT-TERM SEASONAL AND AVERAGE LOAD FORECASTING", booktitle="4th International Conference on Prediction and Nonlinear Dynamics, Nostradamus Prediction Conference", year="2001", pages="1", publisher="n", address="Zlín", isbn="80-7318-030-8" }